面板產業長久以來為台灣重點發展產業，但歷經數次景氣循環與財務不佳的衝擊，加上對岸紅色供應鏈之崛起，原有之市場競爭更加激烈，勢必要另尋新市場來解決產能過剩之問題。因應時代趨勢，面板在智慧生活產品上應用甚多，其中在車用市場方面成為各家面板廠商必爭之地。目前車用面板黑色邊框之製造，以網版印刷製程進行生產，品質依賴工程師的經驗將機台參數與製程條件進行優化，但車用市場規範嚴苛，原有製程能力已無法滿足客戶需求，如何有效提升車用面板網版印刷製程能力成為新課題。
　　本研究之目的在於使用一套實驗設計方法改善車用面板網版印刷製程能力，藉此降低現行黑色邊框內緣油墨溢墨問題，透過假設檢定與部分因子實驗篩檢出顯著因子，並以反應曲面法討論出參數最佳值。其研究流程可作為相關產業人員日後在新產品導入初期製程條件評估使用，研究結果也可作為車用面版、觸控面板網版印刷人員在遇到類似問題時參數調整的依據。過去在網版印刷製程能力提升上，多採用二到三組因子進行小範圍的討論，且沒有考慮到製程原料之影響，並無全面性的探討，且研究過程所使用機台多為手動機台，對於參數值的穩定性上有待提升，對照現況，網印製程使用自動機台進行生產，且取得廠商提供多款原料，進行更全面性的討論。
　　由實驗結果可知，因應製程變更所改良後的油墨種類Type γ有最顯著作用，搭配刮刀速度100 mm/sec、刮刀印壓20kg、刮刀下壓量0.2mm，會有最佳的溢墨反應值92.64 μm，並進行印刷測試證實經驗模型優於原生產參數。 將該參數應用在實際生產後，達到降低擦版頻度33%、產量提升46.7%、不良率下降0.3% 的成效，另外研究方法也可作為日後產品製程改善之參考，藉此提升公司競爭力。

The panel industry has been indispensable in the rapid development of information technology for nearly 20 years. In considering the increasing competition, firms have to deal with production capacity, supply and demand imbalance, and price competition. The emergence of consumer electronic products enhances the panel applications, especially in the automotive panel market, such as central navigation systems and digital dashboards. However, the panel industry encounter challenges of changes in production processes to enhance product quality and correspond to diverse consumer segments, and thus how to improve the process capacity to enhance business competitiveness becomes a critical issue. This study investigates a screen printing process for a high-tech factory, in which the factory mainly produces automotive panel products but encounters the problem of inner edge overflow ink after printing due to changes in the production process. Thus, this study uses the fractional factorial experimental design and response surface methodology to obtain the theoretical model and optimal solutions for the related parameters and improve the problem of inner edge overflow ink. This study considers all related factors that may affect the printing process based on the current condition. In considering costs and machine capacity, we firstly select the factors that result in overflow ink and use the fractional factorial experimental design to investigate the effects that the main factor and other related factors have on the reaction values. Then, we construct first and second order response surface model to examine the obtained significant factor and obtain the empirical model and optimized parameters. The results show that the optimal parameter settings for the ink type (X1) Type γ are the squeegee speed 100 mm / sec, squeegee pressure 20 kg, squeegee under pressure 0.2 mm, and the optimal overflow ink value is 92.64 μm. Moreover, the rubbing frequency can be reduced by 33%, the production capacity can increase 46.7%, and the defect rate can be decreased by 0.3%. Thus, the proposed approach is feasible for future applications in the screen printing process.

中文文獻
向達(民90)。唐代長安與西域文明。石家莊市：河北教育。
成雯(民96)。薄膜開關面板網版印刷要注意的問題。網印工業，30，13-17。
呂政冀(民98)。應用部分因子實驗設計進行LED磊晶之MOCVD製程最佳參數之研
究。國立成功大學工業與資訊管理學系碩士在職專班論文，台南市。
杜玉寶(民98)。智能標籤天線的絲網印刷工藝參數研究。廣東印刷，2，40-41。
邱燕(民104)。晶體矽太陽電池的絲網印刷技術及質量控制。太陽能，1，74-76。
邳守東，黃帥(民102)。絲網印刷中幾種印刷參數與印刷質量關係的研究。電子工業專用設備，5，13-15。
林暉傑(民101)。網版印刷參數最佳化研究。國立成功大學工業與資訊管理學系碩士在職專班論文，台南市。
孟曉華、葛勱衝(民97)。精密絲網印刷技術與設備。電子工業專用設備，37(9)，53-54。
趙海生(民96)。手機面板絲網印刷工藝。印刷雜誌，2，50-52。
張雨寒(民97)。絲網印刷技術的發展和在印製電路板中的應用。計算機與網絡，
34(14)，58-60。
英文文獻
Asilturk, I., & Neseli, S. (2012). Multi response optimisation of CNC turning parameters via Taguchi method-based response surface analysis. Measurement. 45(4), 785-794.
Azam, M., Jahanzaib, M., Wasim, A., & Hussain, S. (2014). Surface roughness modeling using RSM for HSLA steel by coated carbide tools. The International Journal of Advanced Manufacturing Technology. 78(5-8), 1031-1041.
Box, G. E., & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society. Series B (Methodological).13(1), 1-45.
Calise, F., Palombo, A., & Vanoli, L. (2010). Maximization of primary energy savings of solar heating and cooling systems by transient simulations and computer design of experiments. Applied Energy. 87(2), 524-540.
Charusiri, W., & Vitidsant, T. (2005). Kinetic study of used vegetable oil to liquid fuels over sulfated zirconia. Energy & fuels. 19(5), 1783-1789.
Chen, X., Yan, P., Tang, J., Xu, G., & Luo, L. (2012). Development of wafer level glass frit bonding by using barrier trench technology and precision screen printing. Microelectronic Engineering. 100, 6-11
Chiu, K. C. (2003). Application of neural network on LTCC fine line screen printing process. Proceedings of the International Joint Conference on Neural Networks. 2,1043-1047.
Choi, K., Ng, A. H., Fobel, R., Chang-Yen, D. A., Yarnell, L. E., Pearson, E. L., ... & Audet, J. (2013). Automated digital microfluidic platform for magnetic-particle-based immunoassays with optimization by design of experiments. Analytical chemistry. 85(20), 9638-9646.
Ghasemiazar, R., & Azadi, S. (2012). Sensitivity Analysis and Optimization of an Off Road Car Vibration Performance Using DOE and RSM Methods (No. 2012-01-1186). SAE Technical Paper.
Ghiaus, C., & Jabbour, N. (2012). Optimization of multifunction multi-source solar systems by design of experiments. Solar Energy. 86(1), 593-607.
Giovanni, M. (1983). Response surface methodology and product optimization. Food Technology. 11,41-45.
Hamad, A. R., Abboud, J. H., Shuaeib, F. M., & Benyounis, K. Y. (2010). Surface hardening of commercially pure titanium by laser nitriding: response surface analysis. Advances in Engineering Software. 41(4), 674-679.
Hallenbeck, P. C., Grogger, M., Mraz, M., & Veverka, D. (2015). The use of Design of Experiments and Response Surface Methodology to optimize biomass and lipid production by the oleaginous marine green alga, Nannochloropsis gaditana in response to light intensity, inoculum size and CO 2. Bioresource technology. 184, 161-168.
Hill, W. J., & Hunter, W. G. (1966). A review of response surface methodology: a literature survey. Technometrics. 8(4), 571-590.
Kang, H. J., Kim, H. J., Kim, J. S., Choi, W. Y., Chu, W. S., & Ahn, S. H. (2010). Laser marking system for light guide panel using design of experiment and web-based prototyping. Robotics and Computer- Integrated Manufacturing. 26(5), 535-540.
Krebs, F. C., Jørgensen, M., Norrman, K., Hagemann, O., Alstrup, J., Nielsen, T. D., ... & Kristensen, J. (2009). A complete process for production of flexible large area polymer solar cells entirely using screen printing—First public demonstration. Solar Energy Materials & Solar Cell. 93(4),422-441.
Lee, J. H., & Lee, Y. Z. (2015). Development of a diesel cold performance evaluation method by use of design of experiment. International Journal of Automotive Technology. 16(5), 807-812.
Montes, A., Pereyra, C., & de la Ossa, E. M. (2015). Screening design of experiment applied to the supercritical antisolvent precipitation of quercetin. The Journal of Supercritical Fluids. 104, 10-18.
Montgomery, D. C. (2013). Design and analysis of experiments, 8th edition. John Wiley & Sons. New York.
Oke, M. O., Awonorin, S. O., Sanni, L. O., Asiedu, R., & Aiyedun, P. O. (2013). Effect of extrusion variables on extrudates Properties of water yam flour–a response Surface analysis. Journal of Food Processing and Preservation. 37(5), 456-473.
Park, J. H., Kim, K. J., Lee, J. W., & Yoon, J. K. (2015). Light-weight design of automotive suspension link based on design of experiment. International Journal of Automotive Technology. 16(1), 67-71.
Nguyen, T. P., Hilal, N., & Hankins, N. P. (2009). Operating conditions corresponding to optimal final properties of activated sludge using the DOE and RSM techniques. Separation Science and Technology. 44(9), 2041-2066.
Petrick, I. J., & Simpson, T. W. (2013). 3D Printing Disrupts Manufacturing. Research Technology Management. 56(6), 12-16.
Ryu, K., Upadhyaya, A., Upadhyaya, V., Rohatgi, A., & Ok, Y. W. (2015). High efficiency large area n‐type front junction silicon solar cells with boron emitter formed by screen printing technology. Progress in Photovoltaics: Research and Applications. 23(1), 119-123.
Saleem, M. M., & Somá, A. (2015). Design of experiments based factorial design and response surface methodology for MEMS optimization. Microsystem Technologies. 21(1), 263-276.
Thibert, S., Jourdan, J., Bechevet, B., Faissat, J., Mialon, S., Chaussy, D., ... & Beneventi, D. (2013). Combining design of experiments and power loss computations to study the screen printing process. Photovoltaic Specialists Conference(PVSC). 39,2280-2283.
Tiernan, P., Draganescu, B., & Hillery, M. T. (2005). Modelling of extrusion force using the surface response method. The International Journal of Advanced Manufacturing Technology. 27(1-2), 48-52.
Tsioptsias, C., Petridis, D., Athanasakis, N., Lemonidis, I., Deligiannis, A., & Samaras, P. (2015). Post-treatment of molasses wastewater by electrocoagulation and process optimization through response surface analysis. Journal of environmental management. 164, 104-113.
Vadde, K. K., Syrotiuk, V. R., & Montgomery, D. C. (2006). Optimizing Protocol Interaction Using Response Surface Methodology. IEEE Transactions on Mobile Computing, 5(6), 627-639.
Xiao, G., Zhu, Z. (2010). Friction materials development by using DOE/RSM and artificial neural network. Tribology International. 43(1), 218-227.
Zamani, H., Moghiman, M., & Kianifar, A. (2015). Optimization of the parabolic mirror position in a solar cooker using the response surface method (RSM). Renewable Energy. 81, 753-759.